Immune gene expression signature in treg enriched tumor samples

ABSTRACT

An immune gene expression signature is associated with favorable clinical features in Treg-enriched tumor samples and can be used to predict immunogenicity of a tumor, overall survival, and/or chemosensitivity.

This application is a divisional application of currently allowed USpatent application with the Ser. No. 16/767,366, which was filed May 27,2020, which is a 371 application of PCT/US2018/067319, which was filedDec. 21, 2018, and which claims priority to US provisional patentapplication with the Ser. No. 62/613,560, which was filed Jan. 4, 2018.

FIELD OF THE INVENTION

The field of the invention is omics analysis of tumor tissue containingTregs, especially as it relates to gene expression signatures that areassociated with an immunogenic tumor microenvironment and/orchemosensitivity and overall survival.

BACKGROUND OF THE INVENTION

The background description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

All publications and patent applications herein are incorporated byreference to the same extent as if each individual publication or patentapplication were specifically and individually indicated to beincorporated by reference. Where a definition or use of a term in anincorporated reference is inconsistent or contrary to the definition ofthat term provided herein, the definition of that term provided hereinapplies and the definition of that term in the reference does not apply.

Studies of the tumor microenvironment have surfaced promising avenues ofexploration to better understand the clinical relevance of T cell immunebiology. Regulatory T cells (Tregs) have keenly emerged in light oftheir ability to inhibit the adaptive immune response and provide amechanism of immune escape for cancer cells within the tumormicroenvironment across various cancer types. However, the relativelylarge number of studies exploring the clinical relevance of intratumoralTreg abundance has produced controversial results to date, with somestudies finding a poor prognosis associated with Treg infiltration, andothers suggesting a favorable Treg-associated prognosis. Notsurprisingly, the recent efforts to account for these polarized clinicalresults have undermined the notion that FOXP3+ Tregs invariably suppresstumor immunity. To address this uncertainty, multiple gene markers weretaken into account to more accurately identify Tregs, such asFOXP3+BLIMP1 or FOXP3+CTLA4. However, none of the known studies haveproduced results that were suitable to guide a clinician towards arational-based therapy with high confidence in a predicted outcome.

Indeed, immune heterogeneity within the tumor microenvironment has addedmultiple layers of complexity to the understanding of chemosensitivityand survival across various cancer types. Within the tumormicroenvironment, immunogenicity is a favorable clinical feature in partdriven by the antitumor activity of CD8+ T cells. However, tumors ofteninhibit this antitumor activity by exploiting the suppressive functionof Regulatory T cells (Tregs), thus suppressing an adaptive immuneresponse.

Therefore, despite relatively detailed knowledge of Tregs and CD8+ Tcells in isolation, an accurate prediction of immunogenicity of a tumorenriched with Tregs has remained elusive. Thus, there is still a needfor improved systems and methods to better characterize a tumor thatincludes Tregs.

SUMMARY OF THE INVENTION

The inventive subject matter is directed to various systems and methodsof omics analysis and especially expression strength in a tumor sampleof at least two, or at least five, or all of PCDHA5, EFNA5, BARX2, DPP4,CEMP1, SSX1, CD70, LTB, LILRA4, TRAV9.2, GZMM, ZAP70, CD3E, SIRPG, CD3D,SIT1, CD27, CTLA4, ICOS, CD5, GPR171, SH2D1A, TRAT1, ITK, CD3G, RYR1,LAIR2, NTN3, PMCH, GPR1, PLCH2, and BCL11B to determine at least one ofprolonged overall survival of a patient, immunogenicity of the tumor,and sensitivity of the tumor to chemotherapy. In some embodiments,expression strength typically correlates positively with overall patientsurvival, immunogenicity of the tumor, and chemosensitivity of thetumor. Contemplated analyses may further include determination of animmunophenoscore of the tumor.

For example, in one contemplated aspect of the inventive subject matter,the inventors contemplate a method of characterizing a tumor thatincludes a step of quantifying or obtaining expression strength for aplurality of differentially expressed genes, wherein the genes aredifferentially expressed in immune competent cells in the tumor. In afurther step, the expression strengths are associated with a clusterrepresentative of overall patient survival, immunogenicity of the tumor,and/or chemosensitivity of the tumor, and in yet another step, theassociation is used to thereby characterize the tumor as beingassociated with prolonged overall patient survival, immunogenicity ofthe tumor, and chemosensitivity of the tumor. Where desired,contemplated methods may further include a step of calculating animmunophenoscore.

In preferred embodiments, the plurality of differentially expressedgenes comprise at least two, or at least five, or all of PCDHA5, EFNA5,BARX2, DPP4, CEMP1, SSX1, CD70, LTB, LILRA4, TRAV9.2, GZMM, ZAP70, CD3E,SIRPG, CD3D, SIT1, CD27, CTLA4, ICOS, CD5, GPR171, SH2D1A, TRAT1, ITK,CD3G, RYR1, LAIR2, NTN3, PMCH, GPR1, PLCH2, and BCL11B. Most typically,the immune competent cells will include CD8+ T cells, CD4+ T cells, M1macrophages, M2 macrophages, and/or Tregs. As will be readilyappreciated, the step of quantifying or obtaining expression strengthmay use previously obtained transcriptomics data or use quantitative RNAsequencing of nucleic acids obtained from the tumor. Additionally,contemplated methods may include further include a step of administeringchemotherapy or immune therapy upon characterization of the tumor asbeing immunogenic or chemosensitive.

In another aspect of the inventive subject matter, the inventors alsocontemplate a method of treating a patient diagnosed with a tumor thatincludes the steps of (a) quantifying or obtaining expression strengthfor a plurality of differentially expressed genes, wherein the genes aredifferentially expressed in immune competent cells in the tumor; (b)associating the expression strengths with a cluster representative ofoverall patient survival, immunogenicity of the tumor, and/orchemosensitivity of the tumor; (c) using the association to therebycharacterize the tumor as being associated with prolonged overallpatient survival, immunogenicity of the tumor, and chemosensitivity ofthe tumor; and (d) upon characterization of the tumor as beingassociated with prolonged overall patient survival, immunogenicity ofthe tumor, and/or chemosensitivity of the tumor subjecting the patientto chemotherapy or immune therapy.

With respect to the differentially expressed genes and the immunecompetent cells, the same considerations as noted above apply. Likewise,it is noted that the step of quantifying or obtaining expressionstrength may use previously obtained transcriptomics data.

Therefore, the inventors also contemplate the use of expression strengthdata from a tumor sample, wherein the expression strength data aremeasured or obtained from at least two of PCDHA5, EFNA5, BARX2, DPP4,CEMP1, SSX1, CD70, LTB, LILRA4, TRAV9.2, GZMM, ZAP70, CD3E, SIRPG, CD3D,SIT1, CD27, CTLA4, ICOS, CD5, GPR171, SH2D1A, TRAT1, ITK, CD3G, RYR1,LAIR2, NTN3, PMCH, GPR1, PLCH2, and BCL11B to determine prolongedoverall patient survival, immunogenicity of the tumor, and/orchemosensitivity of the tumor.

Most typically, increased expression is associated with the prolongedoverall survival, immunogenicity, and/or chemosensitivity. Suitable usesmay further comprise a determination of an immunophenoscore of thetumor. Moreover, it is noted that the expression strength may bedetermined using RNAseq, or that expression strength is determined frompreviously obtained transcriptomics data.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is an exemplary experimental workflow of 32-gene signatureexpression analysis in discovery and validation cohorts of Treg-enrichedcancer patients.

FIG. 2 depicts unsupervised k-means clusters of Treg-enriched cancerpatients from patient gene expressions of 32-gene signature in thediscovery cohort (n=135). (A) Clusters obtained from k-means clusteringanalysis (k=2), with cluster1 (n=57, high/red) enriched for treatmentsensitivity (P=0.0007, x²=11.58) compared to cluster2 (n=78, low/green),and neither cluster confounded with tumor anatomical location (P>0.05;see Supp. Table 1-2). Row labels correspond to sensitivity marker(‘sens’=Partial Response or Complete Response, ‘res’=Stable Disease orClinical Progressive Disease) and TCGA patient barcodes. Row labelcolors correspond to TCGA cancer type (18 BLCA [red], 37 LUAD [blue], 33PAAD [green], 24 SKCM [purple], 23 STAD [orange]). (B) k-means clustersproduced from 32-gene signature in SKCM (P=0.0450, x²=4.022). (C)k-means clusters produced from 32-gene signature in STAD (P=0.0007,x²=11.53). (D) k-means clusters produced from 32-gene signature in LUAD(P=0.6137, x²=0.225). (E) k-means clusters produced from 32-genesignature in PAAD (P=0.9478, x²=0.004). (F) k-means clusters producedfrom 32-gene signature in BLCA (P=0.0652, x²=3.400).

FIG. 3 depicts overall survival plots/Cox proportional hazard regressionanalysis results. (A) OS analysis between cluster1 patients (n=57, red)and cluster2 patients (n=78, green), P=0.00084, HR=0.40[0.24-0.70]. (B)OS analysis between resistant patients from cluster1 (n=17, red) andresistant patients from cluster2 (n=45, green), P=0.0011,HR=0.30[0.14-0.66].

FIGS. 4A and 4B depict CD8+ T cell activity and immune cell abundanceanalysis between clusters. K-means clusters derived from 32-genesignature expression versus CD8+ T cell activity marker expression for(A) CD8A, (B) CD8B, (C) HLA-A, and (D) PRF1 (P=2.24e-19, P=2.14e-18,P=1.16e-4, and P=2.42e-10, respectively; FIGS. 4A-D). (E) CIBERSORTRNA-seq deconvolution results for relative immune cell type abundancesbetween patient tumor samples from k-means clusters (only differentiallyrepresented cell types (P<0.05) were selected forpresentation/visualization purposes; see Table 1 for absolute cell typeabundances). Treatment resistant patients extracted from k-meansclusters derived from 32-gene signature expression versus CD8+ T cellactivity marker expression for (F) CD8A, (G) CD8B, (H) HLA-A, and (I)PRF1 (P=1.66e-6, P=5.65e-7, P=0.038, and P=5.45e-4, respectively; FIGS.4F-I). (J) CIBERSORT RNA-seq deconvolution results for relative immunecell type abundances between resistant patient tumor samples fromk-means clusters (only differentially represented cell types (P<0.05)were selected for presentation/visualization purposes; see Supp. Table 3for absolute cell type abundances).

FIGS. 5A and 5B depict gene signature analysis in TCGA validationdataset (n=626). (A) 32-gene signature expression k-means clusteringproduces two clusters (n_(cluster1)=332, n_(cluster2)=294). Clustersderived from 32-gene signature expression in TCGA validation data versusCD8+ T cell activity marker expression for (B) CD8A, (C) CD8B, (D)HLA-A, and (E) PRF1 (P=4.19e-79, P=1.59e-69, P=2.96e-17, and P=5.20e-58,respectively; FIGS. 4B-E). (F) CIBERSORT RNA-seq deconvolution resultsfor relative immune cell type abundances between patient tumor samplesfrom k-means clusters (only differentially represented cell types(P<0.05) were selected for presentation/visualization purposes; seeSupp. Table 4 for absolute cell type abundances). (G) Overall survivalplot/Cox proportional hazard regression analysis results betweencluster1 patients (red) and cluster2 patients (green), P=0.00018,HR=0.40[0.49-0.80].

FIGS. 6A and 6B depict patient immunophenoscore and immunotherapy geneset analysis. (A) mean Immunophenoscores represented per cluster derivedas described in methods (bar whiskers represent standard deviations).Normalized gene expression plots between cluster1 and cluster2 patientsfor (B) PD-1 (P<8.34e-15) and (C) CTLA-4 (P<1.83e-3). (D) K-meansclusters produced by each the immunotherapy gene set (19) and our geneset, with both gene sets showing enrichment for chemosensitive tumorsamples (P=0.015, and P=0.0007, respectively). Concordance table showingsimilar unsupervised clusters are produced with ‘good’ concordance(Cohen's kappa=0.64). (E) cluster1 patients possessed significantlyelevated expression for 12 out of 18 genes identified by the 2CT-CRISPRassay system (acceptable P<0.003).

DETAILED DESCRIPTION

The inventors have discovered that an omics signature for Treg-enrichedtumor samples can be established that has significant predictive andprognostic capacity with respect to tumor immunogenicity,chemosensitivity of the tumor to treatment, and/or overall patientsurvival.

More specifically, the inventors analyzed RNA-seq (e.g., qualitativeand/or quantitative transcriptomics) data of Treg-enriched tumor samplesto derive a pan-cancer gene signature in an effort to reconcile theinconsistent results of earlier Treg studies, and to better understandthe variable clinical association of Tregs across alternative tumorcontexts. As is shown in more detail below, an increased expression of a32-gene signature in Treg-enriched tumor samples (n=135) was able todistinguish a cohort of patients associated with chemosensitivity andoverall survival. This cohort was also enriched for CD8+ T cellabundance, as well as the antitumor M1 macrophage subtype. With asubsequent validation in a larger TCGA pool of Treg-enriched patients(n=626), the results presented herein reveal a gene signature that wasable to produce unsupervised clusters of Treg-enriched patients, withone cluster of patients being uniquely representative of an immunogenictumor microenvironment. Ultimately, these results support a genesignature as a putative biomarker to identify certain Treg-enrichedpatients with immunogenic tumors that are more likely to be associatedwith features of favorable clinical outcome.

To that end, the inventors used multiple markers to define Tregenrichment rather than FOXP3 expression alone. However, as opposed toproposing a novel panel of genes to define Tregs, one goal was toexamine the global microenvironmental context through which Tregs may beassociated with differing clinical outcomes such as chemosensitivity andoverall survival (OS), and whether these outcomes would be linked toCD8+ T cell abundance. Therefore, the inventors sought to derive a genesignature that is able to distinguish immunogenic Treg-enriched tumorsamples, and hypothesized that a set of highly variable genesdifferentially expressed by Tregs (amongst 22 immune cell types) wouldbe able to produce distinct patient clusters from a pool of tumorsamples that were all selected due to their enrichment for Tregs. Viewedfrom a different perspective, the inventors implemented a pan-cancerapproach (i.e., an approach that uses data across different tumor types)to identify a favorable immunogenic signature based on immunologicalexpression. Thus, it should be noted that the DNA and RNA data in thetumor samples originated from both tumor cells and immune cells withinthe tumor sample. In the results presented below, a 32-gene signaturewas established that was able to distinguish a ‘hot’ tumor phenotypeassociated with chemosensitivity, OS, and CD8+ T cellactivation/abundance amongst a pool of 135 Treg-enriched patient tumorsamples. The 32-gene signature was validated by confirming associationswith OS, and CD8+ T cell activity/abundance in a larger pool of 626patients. In addition, the inventors overlapped an independentlydiscovered set of genes believed to be essential for CD8+ T cellfunction in immunotherapy and assessed the concordance of the clustersproduced by each gene set independently.

As can be seen in more detail below, the inventors assessed clinicalpotential of a 32-gene expression signature to classify patients andtheir association with chemosensitivity, OS, and CD8+ T cellactivation/abundance. As noted above, the genes chosen to comprise thissignature were differentially expressed by Tregs and were highlyvariable across different Treg-enriched tumor samples. The inventorsobserved that the variation of these genes corresponded to the ‘hot’ vs‘cold’ tumor paradigm, in which a ‘hot’ tumor with a higher level oftumor infiltrating lymphocytes (TILs) is associated with favorableclinical outcomes. It is worth noting that though all patients in thepresent results were enriched for Tregs, the tumor gene signature may bealso associated with a molecular phenotype of Tregs with diminishedsuppressive function.

Despite the proportional significance achieved by the number ofchemosensitive patients between cluster 1 and cluster 2, there remained17 chemoresistant patients in cluster 1. To interrogate these patientsfurther, the inventors conducted a series of parallel analyses betweenthese 17 patients and the 46 resistant patients of cluster 2.Strikingly, it was observed that although labeled as chemoresistant, thecluster 1 resistant patients likely possessed ‘hotter’, more immunogenictumors, and thus survived longer in terms of overall survival.Specifically, the inventors observed favorable tumor infiltratinglymphocyte expression in these 17 tumor samples from a deconvolutionanalysis (e.g., CD8+ T cells, less M0 macrophages), as well as augmentedCD8+ T cell activation expression (e.g., CD8A/B, PRF1, HLA-A). Together,these clinical parameters agreed with associated patient OS. Theseresults suggest that at least in a small proportion of Treg-enrichedpatients, the ‘heat’ of a tumor classified by the 32-gene signature maycomplement the clinical parameter of chemosensitivity, and that thesepresumed chemoresistant patients may have been promising candidates forimmunotherapy as also extrapolated by IPS blockade scores. Indeed, theinventors contemplate that there may be clinical utility indistinguishing chemoresistance in terms of ‘hot’ chemoresistance vs‘cold’ chemoresistance. It is also interesting to note that whilecheckpoint marker expression (e.g., PD-1 and CTLA-4) was highly elevatedin cluster 1 patients, PD-L1 expression was only marginally elevated(P<3.6e-2, data not shown). This difference in significance between PD-1and PD-L1 expression can perhaps be explained by the initialTreg-enrichment filtering.

When differential DNA accessibility was examined between cluster 1patients and cluster 2 patients, a promoter flank site within theTRAF3IP1 gene was predicted to be accessible in almost all cluster 2patients and inaccessible in almost all cluster 1 patients. TRAF3IP1 isa protein that interacts with TRAF3 and is has been observed to inhibitthe innate type I IFN response. TRAF3IP1 was also higher expressed incluster 2 patients who had lower expression of IFNG and anti-tumorimmune activity, suggesting a potential role for TRAF3IP1 regulation ininhibiting the anti-tumor immune response. Additionally, it is worthnoting that the greatest visual difference in accessibility wasspecifically between melanoma patients of cluster 1 and melanomapatients of cluster 2, which is in line with previous work thatdemonstrated accessibility playing a role in CD8+ T cellimmunoreactivity within a melanoma model. Furthermore, recent work inthe B16 melanoma model has argued for the rational of coupling HDACinhibitors to checkpoint blockade therapies to enhance immunotherapyefficacy. This may further propose a clinical relevance for DNAaccessibility to be explored in future studies.

While a literature search to biologically explain each gene of the32-gene signature was not part of the present investigation, one gene ofparticular interest was Lymphotoxin Beta (LTB), which was elevated incluster 1 patient tumor samples. Interestingly, LTB has been shown tospecifically stimulate Tregs to migrate from the tissue to the lymphnodes via afferent lymphatics. This therefore may at least partlyattribute the positive clinical associations of cluster 1 patients toelevated LTB expression, which may be directing immunosuppressive Tregsaway from tumor tissue sites, thus unleashing the antitumor responsewithin cluster 1 tumor samples.

To obtain sufficient sample sizes, the inventors used a pan-cancerapproach to interrogate differences between the clusters produced by theunsupervised k-means method. The inventors confirmed that neithercluster was enriched for any single cancer type, which may haveotherwise surfaced the tumor tissue type as a confounding variable. Theinventors additionally pursued an extensive analysis to de-convolute theheterogeneity of the tumor, showing that CD8+ T cell activity was likelyenriched in cluster 1 patient tumor samples and that chemosensitivitywas perhaps complemented by the immunocompetence of the ‘hot’ tumors.Moreover, in line with the results for each cancer type included in thepan-cancer population, the inventors observed CD8+ T cell tumorabundance to be associated with favorable patient prognosis. Of note,the pro-inflammatory IFNγ, which is a marker of CD8+ T cell mediatedtumor regression and TIL abundance, was significantly elevated incluster 1 samples. It is also worth mentioning that although all patienttumor samples examined were enriched for Treg expression, cluster 1possessed a higher mean CD8+ T cell to Treg ratio (data not shown). Thisresult, which is based on the expression of the 32-gene signature, isalso in line with previous clinical findings pertaining to the tumormicroenvironment. Furthermore, the inventors observed that FOXP3expression was significantly elevated in the clinically favorablecluster of patient tumor samples (cluster 1, P<5.41e-14; data notshown), which is also in accord with previous FOXP3 related studies.

While the inventors specifically interrogated CD8+ T cellabundance/activation markers, the inventors also observed differentialactivation of tumor associated macrophages (TAMs), with cluster 1patients enriched for M1 macrophage abundance. The M1 subtype, which isassociated with higher levels of IL-1, TNFa, IL-12, and CXCL12, isassociated with the inflammatory, anti-tumor response. This observationwas therefore in line with previous work, suggesting a complementaryinteraction between M1 macrophages and CD8+ T cells to amelioratepro-tumorigenic activity within cluster 1 patient tumor samples. Cluster2 accordingly possessed a higher abundance of M0 macrophages,potentially suggesting a larger undifferentiated premature pool ofmonocytes in these patients as opposed to those of cluster 1. A pathwaynetwork activation analysis from a more holistic perspective alsorevealed a shift towards activated TCR signaling and the inflammatoryresponse in cluster 1 patients but not in cluster 2 patients. Together,it was observed that the 32-gene feature set was able to distinguish achemosensitive cohort (cluster 1) with higher antitumor immune activity,perhaps in part via the previously proposed interplay between TAMs andCD8+ T cells. It is also interesting to note that the cluster 2 patientpool (which was enriched for chemoresistance) uniquely possessedupregulated pathway activation for DNA damage response and nucleotidemetabolism (P<0.037 and P<0.026, respectively). In agreement withprevious work, these molecular processes have been observed to play arole in chemoresistance and survival likely by ameliorating the intendeddamage of chemotherapeutic compounds.

It should be noted that the 32-gene signature is specific is toTreg-enriched patient tumor samples as determined by the previouslydescribed GSEA methods, and that the small number of Treg-enrichedpatients with sufficient drug response data was limited (n=135) and thusprevented establishing a robust aggregate cutoff to examine in aheld-out validation dataset. To partially address this, however, theinventors used an unsupervised clustering method which rendered theclusters independent of clinical variables (e.g., tumor stage, lymphnode status, age), and it was shown that the clusters were in goodconcordance with clusters derived from an independently proposed set ofgenes important for CD8+ T cell function. Unexpectedly, thechemosensitivity analysis was not specific to a certain drug, but ratherto response to treatment for BLCA, LUAD, PAAD, SKCM, and STAD patients.This mixed set allowed corroboration of the paradigm of a ‘hot’ tumoracross five tumor types and to propose an immunogenic gene expressionsignature independent of clinical variables.

Therefore, it should be appreciated that the data presented hereinsupport a clinically robust 32-gene signature able to distinguishpatients with a favorable phenotype, at least in part through CD8+ Tcell activity and abundance. Through a pan-cancer analysis ofTreg-enriched patient tumor samples, it can be shown that two opposingcontexts in which Tregs may be associated with alternative clinicalfeatures. Indeed, the inventors' study marks the first pan-cancerpatient study of its kind to interrogate tumor transcriptomic data tohelp reconcile the growing controversy of Tregs and their clinicalimpact. Moreover, the inventors believe the proposed gene signature mayalso serve to extrapolate disease-specific gene signatures that couldfurther sculpt the landscape of tumor immune biology.

Experiments

The following is an exemplary illustration of the inventive subjectmatter and should not be construed as limiting the invention. The personof ordinary skill in the art will readily be able to use alternative orequivalent data, samples, materials, and processes, and al of suchvariations are contemplated herein.

Methods:

Patient Selection and Drug Response Data

The inventors used curated records of drug treatments and outcomesgenerated from TCGA clinical data to label chemosensitivity. Patientswith ‘Complete Response’ or ‘Partial Response’ in this curated datasetwere assigned to a ‘sens’ label, while patients with ‘Stable Disease’ or‘Clinical Progressive disease were assigned to a ‘res’ label.Chemosensitivity served as one parameter to assess clustering outputsignificance (further discussed below).

RNAseq data (TOIL RSEM norm_count) was downloaded and used as inputfeatures for unsupervised clustering was from the TCGA Pan-Cancer cohortfrom xenabrowser.net (dataset ID: tcga_RSEM_Hugo_norm_count, unit: log2(norm_count+1), hub: GA4GH (TOIL) hub). This dataset contained 10,535tumor samples, but only those with available drug response data enrichedfor Tregs were examined. The inventors executed the filtering forTreg-enriched tumor samples via The Cancer Immunome Database (tcia.at)using gene set enrichment analysis (GSEA) of a non-overlapping,pan-cancer derived set of genes representative for Treg enrichment(FOXP3, CCL3L1, CD72, CLEC5A, ITGA4, L1CAM, LIPA, LRP1, LRRC42, MARCO,MMP12, MNDA, MRC1, MS4A6A, PELO, PLEK, PRSS23, PTGIR, ST8SIA4, STAB1).This method has been previously described and clinically validated infurther detail. For a TCGA cohort to be included in the analysis, it wasrequired to comprise samples that were (1) enriched for Tregs (q<0.05),and (2) have sufficient clinical drug response data (at least 15 sampleswith available drug response labels in each cohort). Coalescing andapplying these two filters yielded 135 total patients for analysisacross 5 TCGA cohorts (18 BLCA, 37 LUAD, 33 PAAD, 24 SKCM, 23 STAD).This yielded a sufficient population for a pan-cancer unsupervisedgene-signature expression analysis (n=135).

Gene Signature Selection and Patient Clustering

K-means clustering was used as a vector quantization method to classifythe 135 patient tumor samples (k=2) via the ComplexHeatmap package in Rusing the Treg differentially expressed genes (DEGs) previouslyproduced. However, instead of using all 64 Treg DEGs available, theinventors used only the highly variable 32 genes as a feature gene set(outlier(s) removed and median standard deviation cutoff of 64 genesexpressions within the 135 patient tumor samples). This 32-gene setserved as the feature set used for k-means clustering (the expression ofeach gene was scaled to the z-score relative to expression of that geneacross the 135 tumor samples). As assumed by the unsupervisedclassification process, drug response labels were not included as inputfeatures, and nor were cancer types. Together, the input datasetincluded a table with 135 rows (TCGA patient IDs) and 32 columns (32highly-variable Treg DEGs) of z-score scaled expression values.

Proportional cluster significance for patient drug response sensitivityenrichment was determined using the “N−1” Chi-squared test (DF=1, P<0.05was considered significant). To eliminate the possibility of cancer-typeserving as a confounding variable, and thus ensuring that cohortsanalyzed were not unevenly representative of a certain cancer type, aChi-squared test of independence in a contingency table was performedusing the scipy.stats module in Python (P<0.05 was consideredsignificant).

Survival Analysis

Overall survival (OS) data for the 135 patients was downloaded fromOncoLnc (oncolnc.org). Survival datasets for the BLCA, LUAD, PAAD, SKCM,and STAD cohorts were parsed and Kaplan-Meier plots were generated inPython. Log-rank tests were conducted using the Lifelines implementationin Python (lifelines 0.11.1) (1 month was considered 30 days; P<0.05 wasconsidered significant).

Genomic Transcriptomic Analysis

Patient genomic mutations and transcriptomic expression values wereaccessed using the fbget Python API and parsed in Python(URL.confluence.broadinstitute.org/display/GDAC/fbget). Mutations wereretrieved via whole-exome-sequencing MAF data, and gene expressionvalues were retrieved as log 2-normalized values via RSEM. TheMann-Whitney test was performed to compare mutation counts betweenpatient clusters/cohorts. Two-tailed t-tests were used to determinesignificantly differential expression of CD8A, CD8B, and HLA-A betweencohorts. For both mutation count and differential gene expression,P<0.05 was considered significant. Mutation histograms and expressionswarm-boxplots were generated in Python using the Matplotlib and Seabornlibraries.

Immune-Cell Type Abundance

To characterize immune cell composition within the 135 patient tumorsamples, the inventors used CIBERSORT to present cell-type abundance viaRNA transcript expression from a recommended gene expression profile of547 genes for 22 immune cell types. The recommended model parameterswere used for this prediction task (e.g., LM22 signature gene file,disabled quantile normalization for RNA-seq data), and 1,000permutations were conducted. In short, this strategy applies nu-supportvector regression (v-SVR) to discover a hyperplane that separatesclasses of interest. CIBERSORT has been shown to outperform otherapproaches and has been previously described in further detail.

For upregulated pathway network analysis, the integrated softwareworkflow AltAnalyze was used to analyze expression data in the contextof interaction networks and pathways. The input data includedwhole-transcriptome data from each patient (with 3,325 tissue-specificgenes from TissGDB filtered out) obtained from TOIL RSEM data andencoded as log 2(TPM+1) with no other normalizations.Enriched/upregulated pathways (from WikiPathways) were those with atleast a 2.0-fold-change surfaced from the integrated GO-Elite softwarethat retained significance (P<0.05) after Benjamini-Hochberg Fisher'sexact P-value adjustment.

DNA Accessibility Analysis

DNA accessibility predictions across whole genomes for TCGA patients ofinterest were generated using a convolutional neural network model thathas recently been shown to make accurate predictions at promoter andpromoter flank sites, even for novel biotypes not seen in training. Themodel makes binary accessibility predictions for 600-base-pair DNAsequences centered at potentially accessible sites, as identifiedthrough agglomerative clustering. Model input includes the DNA sequenceof a specific site augmented with an input vector of select geneexpression levels estimated from RNA-seq. This additional RNA-seq vectoris what makes it possible for the neural network to learn to modulateaccessibility predictions appropriately according to the cellularcontext and make predictions for unseen cell types.

Gene expression levels for patient samples were obtained from TOIL RSEMdata and encoded as log 2(TPM+1) with no other normalizations. Sincewhole genome sequencing was not available for all patients of interest,all predictions were made using the reference genome hg19/GRCh37. It istherefore possible that some differences between patients were misseddue to this lack of mutation information. Empirically it was found thatincluding mutation information affected the classification outcome at5.5% of promoter and promoter flank sites.

The model was trained to make predictions at 1.71 million genomic sites,with a training set consisting of 338.7 million training examples,spanning 66 unique cell and tissue types from ENCODE. Of the 1.71million training sites, the TCGA predictions were restricted to 108970sites that overlapped with promoter and promoter flank annotations. Aclassification threshold was selected such that the neural net achieved80% precision on those sites when making predictions for novel biotypes,at which it demonstrated a recall of 65.3% and false positive rate of10%. For analysis only the subset of those promoter and promoter flanksites that overlapped with protein coding genes (as annotated by GENCODEv19 and extended by 1k base pairs front the TSS) were considered,reducing the number of regions to 86057.

Immunophenoscore and Immunotherapy Gene Analysis

An immunophenoscore (IPS) can be derived in an unbiased manner usingmachine learning by considering the four major categories of genes thatdetermine immunogenicity (effector cells, immunosuppressive cells, MHCmolecules, and immunomodulators) by the gene expression of the celltypes these comprise (activated CD4+ T cells, activated CD8+ T cells,effector memory CD4+ T cells, Tregs, MDSCs). The IPS is calculated on a0-10 scale based on representative cell type gene expression z-scores,where higher scores are associated with increased immunogenicity. Thisis because the IPS is positively weighted for stimulatory factors (e.g.,CD8+ T cell gene expression) and negatively weighted for inhibitoryfactors (e.g., MDSC gene expression). Finally, the IPS is calculatedbased on a 0-10 scale relative to the sum of the weighted averagedz-scores. A z-score of 3 or more translates to an IPS of 10, while az-scores 0 or less translates to an IPS of 0, demonstrating a higher IPSis representative of a more immunogenic tumor. This method has beendescribed in further detail with the immunogenic determinant categories,as well as corresponding cell types and gene sets, which can be found attcia.at.

The inventors retrieved patient IPSs from The Cancer Immunome Atlasframework. Relative bar plots were generated for visualization and errorbars reflect standard deviations. Two-tailed t-tests were used todetermine significantly differential IPS values (P<0.05 was consideredsignificant).

The 19 genes used to reinforce the 32-gene signature derived clusterswere taken from a previous study that used a 2CT-CRISPR assay system toidentify 554 candidate genes essential for immunotherapy. Viahierarchical clustering, the gene set we examined were those of the 554genes that identified to be correlated with cytolytic activity acrossmost TCGA cancer types. To measure concordance, the inventors calculatedCohen's kappa via a concordance table of clusters predicted from k-meanswith our 32-gene set and the Patel 18-gene set. A Cohen's kappa value of0.61-0.80 constitutes a ‘good’ quality of agreement.

The heatmap representative of the relative expressions of the 12/18genes significantly elevated in cluster 1 patients (acceptable P<0.003)was produced with the ComplexHeatmap package in R. Clusters representedare those derived from our initial 32-gene k-means clustering. Theinventors used this gene set as validation to show that the 32-genesignature used for clustering was able to produce a cluster with highexpression of the same genes recently identified as likely essential forimmunotherapy in an independent setting.

Results:

Unsupervised clustering using the 32 Treg DEGs: A typical experimentalworkflow is shown in FIG. 1 . Patient tumor samples analyzed in thecurrent study were restricted to those that were (1) Treg-enriched(q<0.05) and (2) possessed sufficient clinical drug response labels(nTotal=135) (FIG. 1 ). Using only the 32 highly variable genesdifferentially expressed by Tregs, k-means clustering was able toproduce two distinct clusters of patient tumor samples (P=0.0007,x2=11.58; FIG. 2 , Panel A). Cluster 1 (n=57) was enriched forchemosensitivity (70.17% of patients were sensitive to drugsprescribed), while cluster 2 (n=78) was enriched for chemoresistance(only 40.74% were clinically sensitive to drugs prescribed).

Overall, patient cancer type was not associated with cluster (P>0.05,x2=7.69; Table 1 below). Nevertheless, when the 32-gene signature wasused to cluster patients of each cancer type individually, clustersenriched for sensitivity to treatment were observed in both SKCM andSTAD (P=0.0450, x2=4.022 and P=0.0007, x2=11.53; FIG. 2 , Panels B-C).Proportional significance was not observed when identical clusteringmethods were applied independently to LUAD, PAAD, and BLCA (P=0.614,P=0.948, and P=0.065, respectively; FIG. 2 , Panels D-F).

Cluster 1 patients are associated with favorable overall survival: Todetermine whether the cluster 1 enrichment for drug-sensitivity wasreflected in survival, overall survival (OS) survival analysis wasperformed and results are shown in FIG. 3 . Cluster 1 OS was observed tobe 29.4 months, while cluster 2 OS was observed shorter at 21.0 months,demonstrating an 8.4-month median survival difference. As hypothesized,cluster 1 was significantly associated with patient OS in comparison tocluster 2 (P=0.00084, HR=0.40[0.24-0.70]; FIG. 3 , Panel A).

While cluster 1 was enriched for patients who were sensitive totreatment, there was a handful of ‘resistant’ patients that were offurther interest in this cluster (n=17). To examine these patientsfurther, the inventors hypothesized that despite clinical labels forinitial drug-response, patient OS would be significantly longer for animmunologically active ‘hotter’ tumor than it would be for a lessimmunologically active ‘colder’ tumor. Therefore, we sought to determinewhether ‘resistant’ patients in cluster 1 differed in OS as compared tothe ‘resistant’ patients of cluster 2 (n=45). After confirming thatneither resistant patient cohort was enriched for a certain cancer type,the inventors analyzed patient OS between each resistant cohort.Strikingly, the median survival duration of the resistant patients incluster1 was more than twice that of the resistant patients in cluster 2(49.0 months vs 18.7 months, P=0.0011, HR=0.30[0.14-0.66]; FIG. 3 ,Panel B).

CD8+ T-cell activity/abundance is enriched in cluster 1 patient tumorsamples: To examine a putative explanation that may corroborate theobserved clinical outcome parameters for the 32-gene signature (e.g.,chemosensitivity and OS), the inventors examined the distributions ofmutation count per cluster and CD8+ T-cell biomarker expression, asmutation count and CD8/HLA-A expression have previously been observed inassociation with drug response and survival via neoantigen productionand cytotoxic lymphocyte activation, respectively. To this end, and inline with this previous work, neither total mutation count nornon-synonymous mutation count was associated with cluster 1. However,CD8A/B, HLA-A, and PRF1 expressions were significantly elevated inpatient tumor samples from cluster 1 (P=2.24e-19, P=2.14e-18, P=1.16e-4,and P=2.42e-10, respectively; see FIG. 4A, Panels A-D).

In order to suggest with further confidence at the level of immune celltype that CD8+ T-cells were more abundant in tumor samples from cluster1patients, the inventors applied a gene expression deconvolutionframework to interpret the immunologically heterogeneous RNA-seq signalsof each patient tumor sample. In line with the observations of elevatedCD8A/B gene expression in cluster 1, CD8+ T-cells mean abundance wassignificantly higher in cluster 1 patient tumor samples than that ofcluster 2 (15.3% vs 7.4% of total immune cells, P=1.00e-7; FIG. 4A,Panel E and Table 1 that indicates RNA-seq tumor sample deconvolution toimmune cell type abundances (absolute value means) between clustersderived from 32-gene signature).

TABLE 1 cluster1 mean abundances cluster2 mean abundances Immune CellType (n = 57) (n = 78) t-statistic P-value T cells CD8 0.153530 0.0738615.270214 0.000001 Macrophages M0 0.108029 0.199764 −4.258066 0.000038 Tcells CD4 memory activated 0.021997 0.006819 4.209816 0.000069Neutrophils 0.001244 0.010078 −3.231105 0.001772 Macrophages M1 0.0724400.048957 2.745808 0.007010 B cells naive 0.103911 0.059839 2.3720210.019814 Mast cells activated 0.001338 0.009605 −2.288763 0.024394Dendritic cells activated 0.014635 0.027883 −2.245423 0.026440 T cellsregulatory (Tregs) 0.020736 0.011808 2.249714 0.026875 Macrophages M20.157614 0.192663 −2.125468 0.035405 Mast cells resting 0.0308080.044771 −2.089000 0.038608 NK cells activated 0.006706 0.011616−1.460647 0.146820 T cells follicular helper 0.045859 0.036308 1.4004190.163802 T cells CD4 naive 0.007653 0.000103 1.341638 0.185125 Monocytes0.026926 0.038978 −1.303338 0.195072 Plasma cells 0.038825 0.050363−1.160674 0.247865 B cells memory 0.038502 0.023567 1.160351 0.248782Dendritic cells resting 0.005879 0.009324 −1.080562 0.281837 NK cellsresting 0.034977 0.033187 0.325962 0.745004 T cells CD4 memory resting0.152862 0.149396 0.207072 0.836325 Eosinophils 0.000260 0.0002230.159053 0.873866

In parallel, continuing the analysis of cluster 1 chemoresistantpatients vs cluster 2 chemoresistant patients, we examined mutationcounts and expression of CD8A/B and HLA-A, as well as immune cell typeabundances between these two resistant patient sub-cohorts. Similar tothe previous broader cluster analysis, mutation count was not associatedwith chemoresistant patient cohort. Moreover, in line with the OSobservations of these patients, CD8A/B, HLA-A, and PRF1 expressions werehigher in resistant patient tumor samples from cluster 1 than for thosefrom cluster 2 (P=9.0e-6, P=2.0e-6, P=0.038, and P=0.0008 respectively;FIG. 4B, Panels F-I). Furthermore, in resistant patients, CD8+ T-cellsmean abundance was higher in cluster1 resistant patient tumor samplesthan that of cluster2 resistant patients (14.5% vs 7.6%, P=0.013; FIG.4B, Panel J).

Patient tumor sample DNA accessibility analysis: DNA accessibilityrefers to whether a certain region of DNA is accessible to regulatorymolecules and proteins such as transcription factors, and recent workhas revealed the importance of investigating the accessibility ofregulatory regions during immune events such as CD8 T cell exhaustion,as accessible regions possess high potential to be expressed and oraccessed by transcriptional machinery that may influence an immuneresponse. In line with this and due to the inherently high dimensionaldata of chromatin organization, deep learning models have been trainedon DNase-seq and recently extended to incorporate RNA-seq data topredict accessibility in a variety of cell types and tumor samples. Theinventors therefore sought to explore whether cluster 1 patients andcluster 2 patients may differ in DNA site accessibility predictionsacross 86,057 promoter and promoter flank sites across the human genome.Interestingly, the inventors observed sites within 14 genes that werepredicted to be enriched for accessibility in cluster 2 patients.

Of particular interest, the inventors observed a site within TRAF3IP1 tobe the most differentially accessible site of all 86,057 sites betweenclusters. TRAF3IP1 has been shown to negatively regulate the innate Type1 IFN response, and indeed, TRAF3IP1 expression was elevated in cluster2 patients. Conversely, when investigating the pro-inflammatory immunemarker IFNG, the inventors observed significant gene expressionelevation in cluster 1 patients. These results suggest that TRAF3IP1 maybe an unfavorable biomarker perhaps related to the anti-tumor immuneresponse at least in Treg-enriched patient tumor samples.

Gene signature analysis in validation cohort: To confirm that theapplication of this gene signature was not unique to the initial subsetof patients selected, and that it extends to all Treg-enriched patientsfrom the cohorts studied, the inventors applied the 32-gene signature tothe remaining Treg-enriched patients. As will be readily appreciated.Treg enrichment can be ascertained in numerous manners, includingimmunohistochemical analysis and expression analysis of genesspecifically or preferentially expressed in Tregs. For analyticalconsistency, these patients were those of the same cancer types (BLCA,LUAD, PAAD, SKCM, STAD) that were Treg-enriched (q<0.05). However, thesepatients did not have available drug response data to assess treatmentsensitivity.

Unsupervised clustering produced two clusters (ncluster 1=332; ncluster2=294) as is shown in FIG. 5A, Panel A. The inventors observed thatcluster 1 patient tumor samples possessed significantly higher levels ofCD8A, CD8B, HLA-A, and PRF1 (P<1.0e-15; FIG. 5A, Panels B-E). Theinventors also elaborated upon these CD8+ T cell activity markers byshowing cluster 1 patients possessed higher abundances of CD8+ T-cellsthan cluster 2 patients (13.5% vs 6.90%; P=2.15e-21; FIG. 5B, Panel F).

Although clinical drug response data was not available for these 636patients, the inventors examined patient OS between the clustersproduced from the 32-gene signature (FIG. 5A, Panel A). Indeed, higherexpression of the gene signature was associated with OS in this largerdataset. The median OS duration for the higher expressing cluster 1patients was significantly longer than that of the cluster 2 patients(P=1.8e-4; FIG. 5B, Panel G), which was in line with the OS resultspreviously discussed (FIG. 3 , Panel A).

Patient immunophenoscore and immunotherapy gene set concordance: Recentwork has shown the utility of the Immunophenoscore (IPS) to predictresponse to immune checkpoint blockade in melanoma patient tumors basedon higher pre-existing immunogenic potential. Therefore, the inventorsreasoned that if cluster1 was indeed representative of a moreimmunogenic phenotype, then cluster 1 tumor samples should too displayan elevated IPS, which has been clinically validated forimmunotherapeutic response. Analysis of patient IPS between cluster 1and cluster 2 revealed significant elevation in the general IPS ofcluster 1 (P=0.019). In addition, the IPS-PD1, IPS-CTLA4, andIPS-PD1/CTLA4 were highly enriched in cluster 1 tumor samples(P=3.68e-10, P=6.00e-6, and 5.52e-12, respectively; FIG. 6A, Panel A).These scores were designed to be assessed for patients with thepotential to be administered checkpoint blockade therapy (e.g.,Nivolumab, Ipilimumab), who possessed elevated expression of PD-1 and orCTLA-4. Indeed, cluster 1 patients possessed significantly higher PD-1and CLTA-4 gene expression than cluster 2 patients (P<8.34e-15 andP<1.83e-13; FIG. 6A, Panels B-C, respectively).

In order to lend further support to the 32-gene signature, the inventorsmeasured the significance of and concordance with another gene setrecently suggested as being essential for effector CD8+ T cell activityfor immunotherapy. This gene set was identified by a 2CT-CRISPR assay ascandidate genes essential for effector CD8+ T cell function and werecorrelated with cytolytic activity across almost all TCGA cancer types.When the inventors applied k-means clustering, this immunotherapy geneset achieved significance in terms of enrichment for chemosensitivetumor samples (P=0.015; FIG. 6B, Panel D), although this was lesssignificant than that of the 32-gene signature (P=0.0007; FIG. 6B, PanelD). More importantly, however, the concordance or agreement betweenpatient tumor sample clustering by each gene set independently was‘good’ (Cohen's kappa=0.64; FIG. 6D), suggesting that the 32-genesignature was able to produce patient tumor sample clusters in concertwith those experimentally derived from an immunotherapy gene setimportant for CD8+ T cell function.

Moreover, and in line with the k-means clustering results, the inventorsobserved that cluster 1 patients possessed significantly elevatedexpression for 12 out of the 18 genes identified by the 2CT-CRISPR assaysystem (acceptable P<0.003; FIG. 6B, Panel E). In concert, pathwaynetwork activation revealed cluster 1 patients to possess enrichedimmune pathway activation via elevated T-Cell Receptor andco-stimulatory signaling, TCR Signaling pathway, and the InflammatoryResponse Pathway (P<0.048, P<0.048, and P<0.043, respectively), andthese pathways were not enriched in cluster 2 patients. Together theseresults support the gene signature as a clinically relevant geneexpression signature of a ‘hot’ immunogenic tumor in Treg-enrichedpatients who may be more likely to respond to checkpoint blockadetherapies targeting PD-1 and or CTLA-4.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. Unless the context dictates the contrary,all ranges set forth herein should be interpreted as being inclusive oftheir endpoints, and open-ended ranges should be interpreted to includecommercially practical values. Similarly, all lists of values should beconsidered as inclusive of intermediate values unless the contextindicates the contrary.

Moreover, all methods described herein can be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g. “such as”) provided with respect to certain embodimentsherein is intended merely to better illuminate the invention and doesnot pose a limitation on the scope of the invention otherwise claimed.No language in the specification should be construed as indicating anynon-claimed element essential to the practice of the invention.

Groupings of alternative elements or embodiments of the inventiondisclosed herein are not to be construed as limitations. Each groupmember can be referred to and claimed individually or in any combinationwith other members of the group or other elements found herein. One ormore members of a group can be included in, or deleted from, a group forreasons of convenience and/or patentability. When any such inclusion ordeletion occurs, the specification is herein deemed to contain the groupas modified thus fulfilling the written description of all Markushgroups used in the appended claims.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the scope of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

What is claimed is:
 1. A method of characterizing a tumor, comprising:quantifying or obtaining expression strength for a plurality ofdifferentially expressed genes, wherein the genes are differentiallyexpressed in immune competent cells in the tumor; associating theexpression strengths with a cluster representative of overall patientsurvival, immunogenicity of the tumor, and/or chemosensitivity of thetumor; and using the association to thereby characterize the tumor asbeing associated with prolonged overall patient survival, immunogenicityof the tumor, and/or chemosensitivity of the tumor.
 2. The method ofclaim 1, further comprising a step of calculating an immunophenoscore.3. The method of claim 1, wherein the plurality of differentiallyexpressed genes comprise at least two of PCDHA5, EFNA5, BARX2, DPP4,CEMP1, SSX1, CD70, LTB, LILRA4, TRAV9.2, GZMM, ZAP70, CD3E, SIRPG, CD3D,SIT1, CD27, CTLA4, ICOS, CD5, GPR171, SH2D1A, TRAT1, ITK, CD3G, RYR1,LAIR2, NTN3, PMCH, GPR1, PLCH2, and BCL11B.
 4. The method of claim 1,wherein the plurality of differentially expressed genes comprise atleast five of PCDHA5, EFNA5, BARX2, DPP4, CEMP1, SSX1, CD70, LTB,LILRA4, TRAV9.2, GZMM, ZAP70, CD3E, SIRPG, CD3D, SIT1, CD27, CTLA4,ICOS, CD5, GPR171, SH2D1A, TRAT1, ITK, CD3G, RYR1, LAIR2, NTN3, PMCH,GPR1, PLCH2, and BCL11B.
 5. The method of claim 1, wherein the pluralityof differentially expressed genes comprise PCDHA5, EFNA5, BARX2, DPP4,CEMP1, SSX1, CD70, LTB, LILRA4, TRAV9.2, GZMM, ZAP70, CD3E, SIRPG, CD3D,SIT1, CD27, CTLA4, ICOS, CD5, GPR171, SH2D1A, TRAT1, ITK, CD3G, RYR1,LAIR2, NTN3, PMCH, GPR1, PLCH2, and BCL11B.
 6. The method of claim 1,wherein the immune competent cells are selected from the groupconsisting of CD8+ T cells, CD4+ T cells, M1 macrophages, M2macrophages, and Tregs.
 7. The method of claim 1, wherein the immunecompetent cells are selected from the group consisting of CD8+ T cells,M1 macrophages, and Tregs.
 8. The method of claim 1, wherein the step ofquantifying or obtaining expression strength uses previously obtainedtranscriptomics data.
 9. The method of claim 1, wherein the step ofquantifying or obtaining expression strength uses a tumor sample. 10.The method of claim 1, wherein the expression strength is determinedfrom RNAseq data.
 11. The method of claim 1, wherein the tumor isenriched in Treg cells.
 12. The method of claim 1, wherein the tumor isenriched in CD8+ T cells.
 13. The method of claim 1, wherein the tumoris enriched in M1 macrophages cells.
 14. The method of claim 1, whereinthe tumor has elevated expression of a check point marker.
 15. Themethod of claim 1, further comprising a step of determining immune cellcomposition in the tumor.
 16. The method of claim 1, further comprisinga step of performing a DNA accessibility prediction on the tumor. 17.The method of claim 1, wherein the plurality of differentially expressedgenes are highly differentially expressed genes in Treg cells.
 18. Themethod of claim 1 wherein the cancer is bladder cancer (BLCA), lungadenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), skin cutaneousmelanoma (SKCM), and stomach adenocarcinoma (STAD).
 19. The method ofclaim 1, further comprising a step of administering chemotherapy uponcharacterization of the tumor as being chemosensitive.
 20. The method ofclaim 1, further comprising a step of administering immune therapy uponcharacterization of the tumor as being immunogenic.